Hire AI Developers: Build AI Solutions with Expert Engineers
- Leanware Editorial Team
- Mar 19
- 11 min read
Updated: Mar 25
Explore AI solutions that drive business growth and automate your company’s internal processes. From fintech trend prediction and sales recommendation engines to AI chatbots and assistants, there’s a world of intelligent solutions to explore.
Partner with Leanware’s expert engineers to develop innovative tools through machine learning, generative AI, natural language processing, and more. Read on to learn more about how to hire the best AI developers for your project.
Why Hire AI Developers?
Hiring skilled AI developers is key if you want your company to compete in the global market. These experts often have niche expertise in a specific industry and can bring valuable insights to your workflows.
The Growing Demand for AI Expertise
With the rapid adoption of AI tools, from chatbots and virtual assistants to data analysis systems, the demand for specialized AI skills has grown by 220% year over year. This shift in business dynamics means more companies want to hire AI engineers who can build these machine learning models and natural language processing (NLP) tools.
Key Benefits of Hiring AI Engineers
Leanware’s AI and Machine Learning specialists bring a lot to the table. So how can your company benefit from hiring our AI engineers?
Pre-Vetted Talent: Leanware has an extensive roster of qualified engineers available to work on new projects. Ready access to this talent pool means we can match developers to clients based on their individual expertise and skill sets
Quick Onboarding: Once we understand your project needs, we’ll put together a dream team that’s ideally suited to your industry, niche, and tech needs. This cuts down on the time you would otherwise spend recruiting qualified talent
Diverse AI Models: Our AI specialists work with a range of AI models, including GPT 4o, LLaMA, Claude, and Grok. This flexibility allows Leanware to deliver personalized AI solutions
Flexible Teams: Leanware’s engagement models include flexible hiring models so you can set up a workflow that aligns with your project management needs, whether that’s a self-managed fully remote team or a closely coordinated nearshoring team.
End-To-End Service: Our expert engineers promise real-time collaboration at every stage of the project’s life cycle, providing timely updates and accounting for client feedback in the development process.
Industries That Benefit from AI Development
The healthcare, finance, and manufacturing sectors each account for around 15% of the AI market share. This is in great part because of the niche specific benefits of AI automation and pattern recognition that help optimize supply chains, forecast market trends, and predict equipment failures.
Industries like legal services can also benefit from compliance assistance as AI adoption speeds up analysis while reducing the risk of manual errors. Sectors like transportation, security, and energy production follow close behind, with improved operational efficiency and reduced costs.
Types of AI Developers You Can Hire
AI developers can fall under various categories, so the first step for anyone who wants to hire artificial intelligence engineers is knowing what you’re looking for.
Machine Learning Engineers
Machine learning engineers work with big data tools and frameworks to create automated analysis solutions. They’re often skilled at using Python, TensorFlow, PyTorch, and cloud-based technologies like Azure.
These algorithms allow machines to draw conclusions from large data sets without being explicitly programmed. This is a broad category that covers learning models like clustering, decision trees, and logical regressions.
Natural Language Processing (NLP) Specialists
Multi-solution NLP models are an incredibly popular AI solution designed to handle various tasks simultaneously. Natural Language Processing Specialists specialize in computational linguistics and are skilled at semantic extraction and n-grams, using tools like ElasticSearch.
They can help you design custom solutions like chatbots to respond to user queries, virtual assistants to interpret voice-based commands, and translators to overcome language barriers.
NLP-based sentiment analysis can also help businesses analyze customer feedback to gauge product satisfaction and improve marketing strategies.
Computer Vision Experts
Computer vision experts design systems that analyze image and video input. These tools can be implemented in facial recognition and object detection software. Additionally, AI application in medical imaging makes it a valuable feature for healthcare software development.
Engineers will often have specialized skill sets, including experience developing image processing algorithms and using deep learning frameworks like TensorFlow.
AI Software Engineers
If you’re looking for a comprehensive service that combines the skill sets of Machine Learning and NLP Specialists, you’ll want to hire an AI Software Engineer. Leanware’s AI Consulting Services handle tasks like API management and continuous integration with AI products. This includes use case identification and tech stack selection to choose the best AI model for your business needs.
Next, the team can integrate the AI algorithm with your existing API or develop a new custom API. Our skilled AI software engineers are experienced with AI frameworks and programming languages like Python and Java.
Deep Learning Engineers
If you’re looking to build a solid team of AI developers for your project, you’ll want to onboard a deep learning engineer. These specialists focus on designing and training deep neural networks to solve complex problems. Their work helps develop NLP applications and image recognition software.
A huge part of their job involves fine-tuning data requirements for projects so the correct variables are being used. They’re also responsible for training and deploying deep learning models that support the scalability needed for production environments.
Data Scientists
Having data scientists on your AI development team is a huge advantage when it comes to extracting critical insights using machine learning models.
Trained to work with large data sets, these professionals work on tasks ranging from anomaly detection to predictive analytics and can help align the technical workings of the model with its intended use cases.
Data Engineers
Your AI model is only as good as the data you feed it, which is why expert data engineers are a valuable resource for any team. Leanware’s data engineers can build reliable data pipelines that collect and sort through relevant data, ensuring accuracy for the model and scalability for the system.
AI Technologies and Tools Used by Developers
Programming Languages: Python, R, Java, C++
These are multiple popular programming languages; however, Python, R, and C++ are the best for AI development projects. Python stands out for its ease of use and extensive libraries, while R is a top choice for statistical analysis.
Additionally, Java is often used to develop enterprise applications where scalability is a priority.
Frameworks: TensorFlow, PyTorch, Keras
Developers often rely on different frameworks based on the requirements of each project. While Keras simplifies the development process for beginners, PyTorch supports flexible testing in research-based projects.
Similarly, TensorFlow is a top choice when it comes to the scalable production of large-scale software.
Cloud AI Services: AWS, Google Cloud, Azure AI
Cloud providers support easy scalability for AI solutions, offering access to virtual technologies without needing extensive in-house infrastructure. AWS, Google Cloud, and Azure AI offer extensive services that support the development and deployment of AI software.
Data Processing & Visualization Tools
The intuitive dashboards in tools like Tableau and ThoughtSpot offer data visualization that helps interpret key insights from complex data sets. These features help business leaders make data-driven decisions that drive growth.
Integration with Existing Models
Developers can use APIs to integrate pre-trained or custom AI models into applications. This allows them to leverage the features of tools like GPT-4 within software solutions, speeding up the development process while saving time.
How to Hire the Best AI Developers
A successful dev project has many stages, and putting together a good team is about a lot more than finding experienced engineers. This five-step process will help make sure your team is prepped for success.
Step 1: Define Your AI Project Needs
Before you hire AI developers, you need clarity regarding your goals, milestones, and project requirements. Document this stage so you have a detailed outline that includes your data needs, scope limitations, compliance benchmarks, etc.
Identifying your objectives and project scope early on will help you recruit the right types of experts for your team. For example, if your project involves natural language processing, look for candidates with experience in text analysis and language modeling.
Step 2: Choose the Right Hiring Model
Hiring models can make or break a project. The main difference often comes down to how you plan to onboard and manage your team.
For example, freelance developers are often deliverables-oriented. While they will meet individual targets, they may struggle to coordinate with each other, compromising the quality of your software.
On the other hand, a dedicated in-house AI development team will not only raise the cost of your project they will also require constant management. The biggest hurdle within in-house teams is finding the right talent, which is an arduous process that can eat up a lot of your time.
A dedicated nearshore team may act as a good compromise. You can outsource both the recruitment and management aspects of development, freeing up time to focus on your concept and larger goals.
Step 3: Source and Vet Top AI Engineers
If you decide to source the team yourself, you’ll need to sift through hundreds of resumes till you find the right candidates. This stage can often take considerable time and drain company resources.
Leanware’s Staff Augmentation Plan is designed for clients who want to speed up hiring and integrate pre-vetted AI engineers into their in-house teams. For clients who want to outsource the hiring, onboarding, and day-to-day oversight, our Managed Team Plan provides a dedicated team for your project and is an excellent hands-off approach
Step 4: Conduct AI Developer Interviews
At this stage, you should reflect on your project needs and identify which types of AI developers you need to onboard. An NLP specialist will have a different skill set from a deep learning engineer.
Pro-tip: Focus on both technical expertise and industry-relevant experience.
Pairing technical questions with practical assessments will help you gauge candidates’ real-world knowledge. Additionally, behavioral or situational questions help identify soft skills like teamwork and communication.
Step 5: Onboard and Integrate AI Developers
While onboarding new team members, try to communicate the role’s responsibilities clearly. Providing specialized training or project documentation can help new developers adapt quickly. Brainstorming sessions are a good way to integrate recent hires within the larger team.
Hiring Models for AI Engineers
Freelance AI Developers
With niche expertise and a catalog of high-quality projects, freelance AI developers bring a lot of technical skills to the table. They’re quick to hire and onboard, delivering fast results on a timeline. However, freelancers are often unavailable or too costly for long-term projects.
Dedicated AI Development Teams
AI Dev Shops are dedicated teams with specialized training that manage AI projects from strategy to deployment. These long-term partnerships provide end-to-end support, allowing clients to focus on ideation and goal setting. Having a dedicated AI dev team often delivers faster outcomes than in-house efforts.
AI Engineers for Long-Term Projects
Senior AI engineers provide constant support and oversight for long-term projects. They’re able to coordinate with remote or in-house teams, guiding new developers and ensuring alignment with the client’s end goals.
Having experienced AI engineers in charge is essential for projects requiring multiple rounds of updates and testing.
Nearshore vs. Offshore AI Development
While both nearshoring and offshoring rely on externally managed remote teams, the difference lies in their location. Offshore teams may be based in distant countries with lower labor costs and flexible hiring policies. While this hiring model can reduce project costs, it adds complications like time differences and language barriers.
Nearshoring seeks to avoid this by minimizing time zone differences. By hiring talent based out of Mexico or Latin America, companies can ensure real-time coordination within the project teams. It acts as a good middle ground between onshoring and offshoring, offering moderate cost savings without compromising project efficiency.
Essential Skills to Look for in an AI Developer
Here are some essential skills you should look out for when you hire artificial intelligence developers.
Strong Mathematical and Statistical Knowledge
An advanced understanding of probability and statistics is essential for AI developers using large data sets to analyze model performance. These basic skills will allow your dev team to interpret and visualize data with tools like Pandas or Matplotlib.
Experience in AI Model Training and Deployment
While theoretical knowledge goes a long way, a qualified AI developer must have hands-on experience in model training for machine learning software. They have to be able to tune hyperparameters and deploy models on cloud platforms like AWS or Google Cloud.
Proficiency in Distributed Computing
Knowledge of distributed computing frameworks will allow your AI developers to design scalable solutions that can handle large volumes of data. You can ask about their expertise using tools like Hadoop and NoSQL databases during the interview process.
Understanding of Ethical AI and Bias Mitigation
Inbuilt biases can ruin the results of your AI model, and mitigation is one of the primary challenges for an AI developer. Your engineer should be aware of how biases occur so they can prepare comprehensive strategies for risk mitigation. Additionally, with AI regulations constantly developing, your dev team should also be mindful of the ethical implications of their work.
AI Development Use Cases
AI-powered chatbots and Virtual Assistants
NLP models for chatbots and virtual assistants are popular AI solutions because of their impact on customer support and workflow management. They provide immediate, personalized support, responding to voice or text commands.
Companies like ING Bank are using GenAI chatbots to cut wait times, assist up to 20% more customers, and provide reliable advice to complex queries.
Predictive Analytics and Data-Driven Decision Making
Machine learning models can analyze large data sets to predict trends, which helps industry leaders make strategic decisions at optimum times.
They can help identify operational lags, sales patterns, and trends in customer behavior. When used correctly, this information can help businesses boost profits and streamline internal workflows.
AI in Healthcare: Diagnostics and Personalized Medicine
Leveraging AI in healthcare allows medical professionals to improve patient success rates by increasing diagnostic accuracy and providing personalized medical plans.
It has remarkable applications in medical image analysis and can design individual treatment regimens that consider a patient’s complete clinical history. Companies like Freenome use AI for early cancer detection, while research centers are using MLA algorithms to predict drug efficacy.
AI for E-Commerce: Recommendation Engines
The application of AI solutions in e-commerce can boost sales for retail businesses. AI models can personalize shopping experiences by suggesting products based on customers’ past purchases or even areas of interest.
These AI-powered recommendation engines can drive sales for businesses while improving user experiences.
AI for Finance: Fraud Detection and Risk Management
Using AI for finance is a good way to reduce the risk of fraudulent reporting in financial statements. For example, you can use machine learning models to predict data accuracy and compliance with international standards. This allows AI tools to detect anomalies by comparing transaction details with data on authorized limits and access.
Start Hiring AI Developers Today!
Use Leanware’s flexible engagement models and start hiring qualified, pre-vetted AI developers today! Our team will help you identify the best resources and experts for your project with a personalized consultation.
Frequently Asked Questions
What programming languages should an AI developer know?
AI developers should be proficient in a range of computing languages normally used to develop AI models. Python is the most popular choice due to its flexibility and extensive open-source libraries. However, R is often used in machine learning models because of its ease of use for statistical analysis and data visualization. Other programming languages include Java and C++.